mlsanityData to 14 Oct 2026

Achieving Hiding and Smart Anti-Jamming Communication: A Parallel DRL Approach against Moving Reactive Jammer

arXiv 2502.02385 ↗First posted 4 Feb 2025cs.IT cs.LG cs.SYcited by 0

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Abstract (arXiv)

This paper addresses the challenge of anti-jamming in moving reactive jamming scenarios. The moving reactive jammer initiates high-power tracking jamming upon detecting any transmission activity, and when unable to detect a signal, resorts to indiscriminate jamming. This presents dual imperatives: maintaining hiding to avoid the jammer's detection and simultaneously evading indiscriminate jamming. Spread spectrum techniques effectively reduce transmitting power to elude detection but fall short in countering indiscriminate jamming. Conversely, changing communication frequencies can help evade indiscriminate jamming but makes the transmission vulnerable to tracking jamming without spread spectrum techniques to remain hidden. Current methodologies struggle with the complexity of simultaneously optimizing these two requirements due to the expansive joint action spaces and the dynamics of moving reactive jammers. To address these challenges, we propose a parallelized deep reinforcement learning (DRL) strategy. The approach includes a parallelized network architecture designed to decompose the action space. A parallel exploration-exploitation selection mechanism replaces the varepsilon -greedy mechanism, accelerating convergence. Simulations demonstrate a nearly 90% increase in normalized throughput.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 54 references that are in the map. First posted on arXiv 4 Feb 2025.

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9 of 54 references.

References · 9

  1. 2017Deep Reinforcement Learning that Matterscited by 754
  2. 2020A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospectscited by 397
  3. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 166
  4. 2019On Empirical Comparisons of Optimizers for Deep Learningcited by 103
  5. 2014Convolutional Neural Networks at Constrained Time Costcited by 81
  6. 2021Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Surveycited by 30
  7. 2020t-Soft Update of Target Network for Deep Reinforcement Learningcited by 17
  8. 2022Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Explorationcited by 11
  9. 2022Game-theoretic Learning Anti-jamming Approaches in Wireless Networkscited by 1

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  1. 2025How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
  2. 2024Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation
  3. 2025Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction
  4. 2026Co-Evolving Zero-Day Jamming: Adaptive Attack Synthesis and Graph Attention-Based Online Detection
  5. 2025A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning
  6. 2025Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning
  7. 2025DRL-Based Maximization of the Sum Cross-Layer Achievable Rate for Networks Under Jamming ↗
  8. 2026Deep Q-Network Based Resilient Drone Communication:Neutralizing First-Order Markov Jammers